Workshop on Context-Aware Recommender Systems (CARS) 2024

Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Alexander Tuzhilin; Moshe Unger

doi:10.1145/3640457.3687102

Workshop on Context-Aware Recommender Systems (CARS) 2024

Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger

Information and Decision Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

Original language	English (US)
Title of host publication	RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery, Inc
Pages	1219-1221
Number of pages	3
ISBN (Electronic)	9798400705052
DOIs	https://doi.org/10.1145/3640457.3687102
State	Published - Oct 8 2024
Event	18th ACM Conference on Recommender Systems, RecSys 2024 - Bari, Italy Duration: Oct 14 2024 → Oct 18 2024

Publication series

Name	RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems

Conference

Conference	18th ACM Conference on Recommender Systems, RecSys 2024
Country/Territory	Italy
City	Bari
Period	10/14/24 → 10/18/24

Bibliographical note

Publisher Copyright:
© 2024 Copyright held by the owner/author(s).

Keywords

Context
Context-Aware Recommendation
Contextual Modeling
Sequence-Aware Recommendation

Publisher link

10.1145/3640457.3687102

Find It

Find It at U of M Libraries

Cite this

Adomavicius, G., Bauman, K., Mobasher, B., Tuzhilin, A., & Unger, M. (2024). Workshop on Context-Aware Recommender Systems (CARS) 2024. In RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1219-1221). (RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/3640457.3687102

Workshop on Context-Aware Recommender Systems (CARS) 2024. / Adomavicius, Gediminas; Bauman, Konstantin; Mobasher, Bamshad et al.
RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems. Association for Computing Machinery, Inc, 2024. p. 1219-1221 (RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Adomavicius, G, Bauman, K, Mobasher, B, Tuzhilin, A & Unger, M 2024, Workshop on Context-Aware Recommender Systems (CARS) 2024. in RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems. RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, pp. 1219-1221, 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, 10/14/24. https://doi.org/10.1145/3640457.3687102

@inproceedings{be7080d80e2c4879a2847cf739567a53,

title = "Workshop on Context-Aware Recommender Systems (CARS) 2024",

abstract = "Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.",

keywords = "Context, Context-Aware Recommendation, Contextual Modeling, Sequence-Aware Recommendation",

author = "Gediminas Adomavicius and Konstantin Bauman and Bamshad Mobasher and Alexander Tuzhilin and Moshe Unger",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 18th ACM Conference on Recommender Systems, RecSys 2024 ; Conference date: 14-10-2024 Through 18-10-2024",

year = "2024",

month = oct,

day = "8",

doi = "10.1145/3640457.3687102",

language = "English (US)",

series = "RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery, Inc",

pages = "1219--1221",

booktitle = "RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems",

}

TY - GEN

T1 - Workshop on Context-Aware Recommender Systems (CARS) 2024

AU - Adomavicius, Gediminas

AU - Bauman, Konstantin

AU - Mobasher, Bamshad

AU - Tuzhilin, Alexander

AU - Unger, Moshe

PY - 2024/10/8

Y1 - 2024/10/8

N2 - Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

AB - Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

KW - Context

KW - Context-Aware Recommendation

KW - Contextual Modeling

KW - Sequence-Aware Recommendation

UR - http://www.scopus.com/inward/record.url?scp=85210495193&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85210495193&partnerID=8YFLogxK

U2 - 10.1145/3640457.3687102

DO - 10.1145/3640457.3687102

M3 - Conference contribution

AN - SCOPUS:85210495193

T3 - RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems

SP - 1219

EP - 1221

BT - RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems

PB - Association for Computing Machinery, Inc

T2 - 18th ACM Conference on Recommender Systems, RecSys 2024

Y2 - 14 October 2024 through 18 October 2024

ER -

Workshop on Context-Aware Recommender Systems (CARS) 2024

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this